When a fingerprint verification apparatus is used to identify a person, the following process is generally performed. First, the degree of consistency (the match rate) between an input fingerprint pattern obtained by a sensor or the like and a registered fingerprint pattern that has been registered in advance is computed. Then, the match rate is compared with a predetermined threshold to determine whether the input fingerprint pattern and the registered fingerprint pattern are of the same person. Based on the determination result, the person is identified. In this personal identification process, the rate at which an input fingerprint pattern of a person is falsely determined to be identical with a registered fingerprint pattern of another person is called the “false acceptance rate.”
In this type of fingerprint verification apparatus, the predetermined threshold is often uniformly fixed to a certain value irrespective of which finger is verified. However, the match rate between the fingerprint patterns actually varies among persons (fingers). That is, some people have fingerprints that provide a high match rate, while other people have fingerprints that only provide a low match rate. Therefore, if the threshold is set to a higher value, the fingerprint verification apparatus tends to reject authentication of a wrong person, but it also tends to falsely reject authentication of the genuine person. Conversely, if the threshold is set to a lower value, the fingerprint verification apparatus tends to accept authentication of the genuine person, but it also tends to falsely accept authentication of a wrong person. This will be a cause of reduction in the identification success rate.
According to a data recognition method disclosed in Patent Document 1 (Japanese Patent Application Laid-Open No. 2000-215313), the match rate between each registered data item and other registered data items is computed. Then, based on a match rate distribution obtained for each registered data item, a threshold for the registered data item is generated. When a person is identified, the match rate between a verification target data to be recognized and a corresponding candidate data item in the registered data items is computed. The computed match rate is compared with the threshold for the candidate data item to determine whether the verification target data and the candidate data correspond to each other. In this data recognition method of Patent Document 1, a target value for the false acceptance probability is given first, and the lowest match rate that meets the target value is dynamically computed as the threshold. Thus, the threshold is different for each registered data item.
A pattern recognition apparatus disclosed in Patent Document 2 (Japanese Patent Application Laid-Open No. 2002-230551) involves, for a certain set of patterns, determining a difference between the feature vector of each pattern and the average feature vector of each correct category. This produces a set of difference vectors. An error distribution corresponding to this set of difference vectors is used as a probability density function to perform pattern recognition.
An object of the present invention is to provide a pattern recognition system, a pattern recognition method, and a pattern recognition program capable of increasing the accuracy in computing the false acceptance probability.
Another object of the present invention is to provide a pattern recognition system, a pattern recognition method, and a pattern recognition program capable of ensuring stable security strength.
Still another object of the present invention is to provide a pattern recognition system, a pattern recognition method, and a pattern recognition program capable of reducing the learning cost of pattern recognition.